OpenRaiser/Intern-Atlas huggingface dataset:
Mugshot Identification from Manipulated Facial Images
Venue: arXiv cs.CV 2012
Year: 2012

Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known eigenface approach. Experiments have been conducted with real case images to evaluate the performance of both methods.
Large Scale Language Modeling in Automatic Speech Recognition
Venue: arXiv cs.CL 2012
Year: 2012

Large language models have been proven quite beneficial for a variety of automatic speech recognition tasks in Google. We summarize results on Voice Search and a few YouTube speech transcription tasks to highlight the impact that one can expect from increasing both the amount of training data, and the size of the language model estimated from such data. Depending on the task, availability and amount of training data used, language model size and amount of work and care put into integrating them in the lattice rescoring step we observe reductions in word error rate between 6% and 10% relative, for systems on a wide range of operating points between 17% and 52% word error rate.
Very Low-Rate Variable-Length Channel Quantization for Minimum Outage Probability
Venue: arXiv cs.IT 2012
Year: 2012

We identify a practical vector quantizer design problem where any fixed-length quantizer (FLQ) yields non-zero distortion at any finite rate, while there is a variable-length quantizer (VLQ) that can achieve zero distortion with arbitrarily low rate. The problem arises in a $t \times 1$ multiple-antenna fading channel where we would like to minimize the channel outage probability by employing beamforming via quantized channel state information at the transmitter (CSIT). It is well-known that in such a scenario, finite-rate FLQs cannot achieve the full-CSIT (zero distortion) outage performance. We construct VLQs that can achieve the full-CSIT performance with finite rate. In particular, with $P$ denoting the power constraint of the transmitter, we show that the necessary and sufficient VLQ rate that guarantees the full-CSIT performance is $Θ(1/P)$. We also discuss several extensions (e.g. to precoding) of this result.
Randomness and Non-determinism
Venue: arXiv cs.CC 2012
Year: 2012

Exponentiation makes the difference between the bit-size of this line and the number (<< 2^{300}) of particles in the known Universe. The expulsion of exponential time algorithms from Computer Theory in the 60's broke its umbilical cord from Mathematical Logic. It created a deep gap between deterministic computation and -- formerly its unremarkable tools -- randomness and non-determinism. Little did we learn in the past decades about the power of either of these two basic "freedoms" of computation, but some vague pattern is emerging in relationships between them. The pattern of similar techniques instrumental for quite different results in this area seems even more interesting. Ideas like multilinear and low-degree multivariate polynomials, Fourier transformation over low-periodic groups seem very illuminating. The talk surveyed some recent results. One of them, given in a stronger form than previously published, is described below.
Public key Steganography Using Discrete Cross-Coupled Chaotic Maps
Venue: arXiv cs.CR 2012
Year: 2012

By cross-coupling two logistic maps a novel method is proposed for the public key steganography in JPEG image. Chaotic maps entail high complexity in the used algorithm for embedding secret data in a medium. In this paper, discrete cross- coupled chaotic maps are used to specifying the location of the different parts of the secret data in the image. Modifying JPEG format during compressing and decompressing, and also using public key enhanced difficulty of the algorithm. Simulation results show that in addition to excessive capacity, this method has high robustness and resistance against hackers and can be applicable in secret communication. Also the PSNR value is high compared to the other works.
Multi-Stratum Networks: toward a unified model of on-line identities
Venue: arXiv cs.SI 2012
Year: 2012

One of the reasons behind the success of Social Network Analysis is its simple and general graph model made of nodes (representing individuals) and ties. However, when we focus on our daily on-line experience we must confront a more complex scenario: people inhabitate several on-line spaces interacting to several communities active on various technological infrastructures like Twitter, Facebook, YouTube or FourSquare and with distinct social objectives. This constitutes a complex network of interconnected networks where users' identities are spread and where information propagates navigating through different communities and social platforms. In this article we introduce a model for this layered scenario that we call multi-stratum network. Through a theoretical discussion and the analysis of real-world data we show how not only focusing on a single network may provide a very partial understanding of the role of its users, but also that considering all the networks separately may not reveal the information contained in the whole multi-stratum model.
Rating through Voting: An Iterative Method for Robust Rating
Venue: arXiv cs.IR 2012
Year: 2012

In this paper we introduce an iterative voting algorithm and then use it to obtain a rating method which is very robust against collusion attacks as well as random and biased raters. Unlike the previous iterative methods, our method is not based on comparing submitted evaluations to an approximation of the final rating scores, and it entirely decouples credibility assessment of the cast evaluations from the ranking itself. The convergence of our algorithm relies on the existence of a fixed point of a continuous mapping which is also a stationary point of a constrained optimization objective. We have implemented and tested our rating method using both simulated data as well as real world data. In particular, we have applied our method to movie evaluations obtained from MovieLens and compared our results with IMDb and Rotten Tomatoes movie rating sites. Not only are the ratings provided by our system very close to IMDb rating scores, but when we differ from the IMDb ratings, the direction of such differences is essentially always towards the ratings provided by the critics in Rotten Tomatoes. Our tests demonstrate high efficiency of our method, especially for very large online rating systems, for which trust management is both of the highest importance and one of the most challenging problems.
Learning classifier systems with memory condition to solve non-Markov problems
Venue: arXiv cs.NE 2012
Year: 2012

In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent's sensation, which are also known as non-Markov environments. Within these environments, XCS either fails, or only develops a suboptimal policy, since it has no memory. In this work, we develop a new classifier system based on XCS to tackle this problem. It adds an internal message list to XCS as the memory list to record input sensation history, and extends a small number of classifiers with memory conditions. The classifier's memory condition, as a foothold to disambiguate non-Markov states, is used to sense a specified element in the memory list. Besides, a detection method is employed to recognize non-Markov states in environments, to avoid these states controlling over classifiers' memory conditions. Furthermore, four sets of different complex maze environments have been tested by the proposed method. Experimental results show that our system is one of the best techniques to solve partially observable environments, compared with some well-known classifier systems proposed for these environments.
Detecting English Writing Styles For Non-native Speakers
Venue: arXiv cs.CL 2012
Year: 2012

Analyzing writing styles of non-native speakers is a challenging task. In this paper, we analyze the comments written in the discussion pages of the English Wikipedia. Using learning algorithms, we are able to detect native speakers' writing style with an accuracy of 74%. Given the diversity of the English Wikipedia users and the large number of languages they speak, we measure the similarities among their native languages by comparing the influence they have on their English writing style. Our results show that languages known to have the same origin and development path have similar footprint on their speakers' English writing style. To enable further studies, the dataset we extracted from Wikipedia will be made available publicly.
Small Cell Deployments: Recent Advances and Research Challenges
Venue: arXiv cs.NI 2012
Year: 2012

This paper summarizes the outcomes of the 5th International Workshop on Femtocells held at King's College London, UK, on the 13th and 14th of February, 2012.The workshop hosted cutting-edge presentations about the latest advances and research challenges in small cell roll-outs and heterogeneous cellular networks. This paper provides some cutting edge information on the developments of Self-Organizing Networks (SON) for small cell deployments, as well as related standardization supports on issues such as carrier aggregation (CA), Multiple-Input-Multiple-Output (MIMO) techniques, and enhanced Inter-Cell Interference Coordination (eICIC), etc. Furthermore, some recent efforts on issues such as energy-saving as well as Machine Learning (ML) techniques on resource allocation and multi-cell cooperation are described. Finally, current developments on simulation tools and small cell deployment scenarios are presented. These topics collectively represent the current trends in small cell deployments.
Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images
Venue: arXiv cs.CV 2012
Year: 2012

Remote sensing is a technology to acquire data for disatant substances, necessary to construct a model knowledge for applications as classification. Recently Hyperspectral Images (HSI) becomes a high technical tool that the main goal is to classify the point of a region. The HIS is more than a hundred bidirectional measures, called bands (or simply images), of the same region called Ground Truth Map (GT). But some bands are not relevant because they are affected by different atmospheric effects; others contain redundant information; and high dimensionality of HSI features make the accuracy of classification lower. All these bands can be important for some applications; but for the classification a small subset of these is relevant. The problematic related to HSI is the dimensionality reduction. Many studies use mutual information (MI) to select the relevant bands. Others studies use the MI normalized forms, like Symmetric Uncertainty, in medical imagery applications. In this paper we introduce an algorithm based also on MI to select relevant bands and it apply the Symmetric Uncertainty coefficient to control redundancy and increase the accuracy of classification. This algorithm is feature selection tool and a Filter strategy. We establish this study on HSI AVIRIS 92AV3C. This is an effectiveness, and fast scheme to control redundancy.
Enhancing Invenio Digital Library With An External Relevance Ranking Engine
Venue: arXiv cs.IR 2012
Year: 2012

Invenio is a comprehensive web-based free digital library software suite originally developed at CERN. In order to improve its information retrieval and word similarity ranking capabilities, the goal of this thesis is to enhance Invenio by bridging it with modern external information retrieval systems. In the first part a comparison of various information retrieval systems such as Solr and Xapian is made. In the second part a system-independent bridge for word similarity ranking is designed and implemented. Subsequently, Solr and Xapian are integrated in Invenio via adapters to the bridge. In the third part scalability tests are performed. Finally, a future outlook is briefly discussed.
A Threshold For Clusters in Real-World Random Networks
Venue: arXiv cs.SI 2012
Year: 2012

Recent empirical work [Leskovec2009] has suggested the existence of a size threshold for the existence of clusters within many real-world networks. We give the first proof that this clustering size threshold exists within a real-world random network model, and determine the asymptotic value at which it occurs. More precisely, we choose the Community Guided Attachment (CGA) random network model of Leskovek, Kleinberg, and Faloutsos [Leskovec2005]. The model is non-uniform and contains self-similar communities, and has been shown to have many properties of real-world networks. To capture the notion of clustering, we follow Mishra et. al. [Mishra2007], who defined a type of clustering for real-world networks: an (α,β)-cluster is a set that is both internally dense (to the extent given by the parameter β), and externally sparse (to the extent given by the parameter α) . With this definition of clustering, we show the existence of a size threshold of (\ln n)^{1/2} for the existence of clusters in the CGA model. For all ε>0, a.a.s. clusters larger than (\ln n)^{1/2-ε} exist, whereas a.a.s. clusters larger than (\ln n)^{1/2+ε} do not exist. Moreover, we show a size bound on the existence of small, constant-size clusters.
An Elementary Derivation of Mean Wait Time in Polling Systems
Venue: arXiv cs.SY 2012
Year: 2012

Polling systems are a well-established subject in queueing theory. However, their formal treatments generally rely heavily on relatively sophisticated theoretical tools, such as moment generating functions and Laplace transforms, and solutions often require the solution of large systems of equations. We show that, if you are willing to only have the average waiting of a system time rather than higher moments, it can found through an elementary derivation based only on algebra and some well-known properties of Poisson processes. Our result is simple enough to be easily used in real-world applications, and the simplicity of our derivation makes it ideal for pedagogical purposes.
Majority is Stablest : Discrete and SoS
Venue: arXiv cs.CC 2012
Year: 2012

The Majority is Stablest Theorem has numerous applications in hardness of approximation and social choice theory. We give a new proof of the Majority is Stablest Theorem by induction on the dimension of the discrete cube. Unlike the previous proof, it uses neither the "invariance principle" nor Borell's result in Gaussian space. The new proof is general enough to include all previous variants of majority is stablest such as "it ain't over until it's over" and "Majority is most predictable". Moreover, the new proof allows us to derive a proof of Majority is Stablest in a constant level of the Sum of Squares hierarchy.This implies in particular that Khot-Vishnoi instance of Max-Cut does not provide a gap instance for the Lasserre hierarchy.
A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions
Venue: arXiv cs.NE 2012
Year: 2012

In the field of empirical modeling using Genetic Programming (GP), it is important to evolve solution with good generalization ability. Generalization ability of GP solutions get affected by two important issues: bloat and over-fitting. We surveyed and classified existing literature related to different techniques used by GP research community to deal with these issues. We also point out limitation of these techniques, if any. Moreover, the classification of different bloat control approaches and measures for bloat and over-fitting are also discussed. We believe that this work will be useful to GP practitioners in following ways: (i) to better understand concepts of generalization in GP (ii) comparing existing bloat and over-fitting control techniques and (iii) selecting appropriate approach to improve generalization ability of GP evolved solutions.
Visual Transfer Learning: Informal Introduction and Literature Overview
Venue: arXiv cs.CV 2012
Year: 2012

Transfer learning techniques are important to handle small training sets and to allow for quick generalization even from only a few examples. The following paper is the introduction as well as the literature overview part of my thesis related to the topic of transfer learning for visual recognition problems.
Wireless Compressive Sensing for Energy Harvesting Sensor Nodes
Venue: arXiv cs.IT 2012
Year: 2012

We consider the scenario in which multiple sensors send spatially correlated data to a fusion center (FC) via independent Rayleigh-fading channels with additive noise. Assuming that the sensor data is sparse in some basis, we show that the recovery of this sparse signal can be formulated as a compressive sensing (CS) problem. To model the scenario in which the sensors operate with intermittently available energy that is harvested from the environment, we propose that each sensor transmits independently with some probability, and adapts the transmit power to its harvested energy. Due to the probabilistic transmissions, the elements of the equivalent sensing matrix are not Gaussian. Besides, since the sensors have different energy harvesting rates and different sensor-to-FC distances, the FC has different receive signal-to-noise ratios (SNRs) for each sensor. This is referred to as the inhomogeneity of SNRs. Thus, the elements of the sensing matrix are also not identically distributed. For this unconventional setting, we provide theoretical guarantees on the number of measurements for reliable and computationally efficient recovery, by showing that the sensing matrix satisfies the restricted isometry property (RIP), under reasonable conditions. We then compute an achievable system delay under an allowable mean-squared-error (MSE). Furthermore, using techniques from large deviations theory, we analyze the impact of inhomogeneity of SNRs on the so-called k-restricted eigenvalues, which governs the number of measurements required for the RIP to hold. We conclude that the number of measurements required for the RIP is not sensitive to the inhomogeneity of SNRs, when the number of sensors n is large and the sparsity of the sensor data (signal) k grows slower than the square root of n. Our analysis is corroborated by extensive numerical results.
Proof Hints for Event-B
Venue: arXiv cs.SE 2012
Year: 2012

Interactive proofs are often considered as costs of formal modelling activity. In an incremental development environment such as the Rodin platform for Event-B, information from proof attempts is important input for adapting the model. This paper considers the idea of using interactive proofs to "improve" the model, in particular, to convert them into automatic ones. We propose to lift some essential proof information from the interactive proofs into the model as what we called proof hints. In particular, proof hints are not only for the purpose of proofs: it helps to understand the formal models better.
Advance Reservation based DAG Application Scheduling Simulator for Grid Environment
Venue: arXiv cs.DC 2012
Year: 2012

In the last decade, scheduling of Directed Acyclic Graph (DAG) application in the context of Grid environment has attracted attention of many researchers. However, deployment of Grid environment requires skills, efforts, budget, and time. Although various simulation toolkits or frameworks are available for simulating Grid environment, either they support different possible studies in Grid computing area or takes lot of efforts in molding them to make them suitable for scheduling of DAG application. In this paper, we describe design and implementation of GridSim based ready to use application scheduler for scheduling of DAG application in Grid environment. The proposed application scheduler supports supplying DAG application and configuration of Grid resources through GUI. We also describe implementation of Min-Min static scheduling algorithm for scheduling of DAG application to validate the proposed scheduler. Our proposed DAG application scheduling simulator is useful, easy, and time-saver.
Data Shapes and Data Transformations
Venue: arXiv cs.DB 2012
Year: 2012

Nowadays, information management systems deal with data originating from different sources including relational databases, NoSQL data stores, and Web data formats, varying not only in terms of data formats, but also in the underlying data model. Integrating data from heterogeneous data sources is a time-consuming and error-prone engineering task; part of this process requires that the data has to be transformed from its original form to other forms, repeating all along the life cycle. With this report we provide a principled overview on the fundamental data shapes tabular, tree, and graph as well as transformations between them, in order to gain a better understanding for performing said transformations more efficiently and effectively.
Data-parallel programming with Intel Array Building Blocks (ArBB)
Venue: arXiv cs.PF 2012
Year: 2012

Intel Array Building Blocks is a high-level data-parallel programming environment designed to produce scalable and portable results on existing and upcoming multi- and many-core platforms. We have chosen several mathematical kernels - a dense matrix-matrix multiplication, a sparse matrix-vector multiplication, a 1-D complex FFT and a conjugate gradients solver - as synthetic benchmarks and representatives of scientific codes and ported them to ArBB. This whitepaper describes the ArBB ports and presents performance and scaling measurements on the Westmere-EX based system SuperMIG at LRZ in comparison with OpenMP and MKL.
A New Randomness Evaluation Method with Applications to Image Shuffling and Encryption
Venue: arXiv cs.CR 2012
Year: 2012

This letter discusses the problem of testing the degree of randomness within an image, particularly for a shuffled or encrypted image. Its key contributions are: 1) a mathematical model of perfectly shuffled images; 2) the derivation of the theoretical distribution of pixel differences; 3) a new $Z$-test based approach to differentiate whether or not a test image is perfectly shuffled; and 4) a randomized algorithm to unbiasedly evaluate the degree of randomness within a given image. Simulation results show that the proposed method is robust and effective in evaluating the degree of randomness within an image, and may often be more suitable for image applications than commonly used testing schemes designed for binary data like NIST 800-22. The developed method may be also useful as a first step in determining whether or not a shuffling or encryption scheme is suitable for a particular cryptographic application.